CONFLATION OF NATIONAL BRIDGE INVENTORY DATABASE WITH TIGERBASED ROAD VECTORS
Bibliographic record
Abstract
Abstract. The National Bridge Inventory (NBI) database provides detailed bridge information with full coverage in the United States. In the database, each bridge record has an associated geographic location, which makes it possible to match the NBI records with road vectors in a geospatial database. This paper presents our approach to conflating the NBI database with U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) road vectors based on a point-to-line matching algorithm, which combines both geometric and semantic similarity measurements. Experiments have shown that most NBI bridges can be successfully matched to TIGER road vectors with an overall success rate over 80%. The matched results are then used to help define linear features for bridges in the road database. The resultant road databases have enhanced bridge information and are further used in creating a 3D road database. The proposed methodology has been fully implemented and has been used in various applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".